Skill UI

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

This skill provides comprehensive guidelines for monitoring Granola usage, tracking team meeting patterns, and building detailed analytics dashboards. It guides users in defining critical metrics (e.g., adoption rate, efficiency score) and establishing robust data pipelines. Users can stream meeting metadata from Granola to data warehouses like BigQuery, enabling deep, customizable reporting and automated weekly insights via services like Zapier and Slack.

This tool provides comprehensive usage analytics and reporting for Kling AI video generation. It logs detailed events (submission, completion, errors) into structured JSONL files. Users can track performance metrics, analyze success rates, monitor total resource consumption (credits), and generate cost summaries over defined periods. Ideal for building dashboards, cost auditing, and understanding usage patterns.

This guide provides comprehensive patterns and best practices for designing high-impact Key Performance Indicator (KPI) dashboards. It covers everything from establishing a KPI framework (Strategic, Tactical, Operational) to defining departmental metrics (Sales, Marketing, Finance) and implementing best-practice layouts for executive summaries and SaaS metrics. Use this skill to translate raw data into actionable business insights.

This guide details how to implement comprehensive observability for AI agents. It covers monitoring key metrics such as task success rates, step failures, execution duration, and credit consumption. Use this when setting up performance dashboards, configuring real-time alerts (via webhooks/Prometheus), or tracking agent reliability over time.

This skill provides comprehensive tracking and analysis of OpenRouter API usage metrics. It collects critical data points such as per-request total cost, prompt and completion token counts, latency, and usage patterns. Use this skill when building financial dashboards, optimizing AI spending, monitoring API consumption, or generating detailed usage reports.

This skill provides a comprehensive toolkit for calculating essential portfolio risk metrics. It includes Value at Risk (VaR), Conditional VaR (CVaR), Sharpe Ratio, Sortino Ratio, and drawdown analysis. It is crucial for measuring overall portfolio risk, implementing risk limits, building sophisticated risk dashboards, and ensuring compliance for regulatory reporting in finance.

Automated help for data analytics visualization best practices, offering step-by-step guidance, industry patterns, production-ready code, and validation for dashboards and reports.

Provides open REST access to the U.S. Treasury Fiscal Data API so analysts can pull national debt, spending, revenue, interest rates, exchange rates, auctions, and savings bond statistics without an API key; useful for financial dashboards, debt tracking, and research involving federal fiscal metrics.

PyGraphistry is a Python interface to the Graphistry platform for GPU-accelerated graph visualization, letting analysts render millions of nodes/edges in interactive dashboards to surface anomalies and relationships quickly.

A comprehensive guide to product analytics, covering essential concepts like event tracking (PostHog, Mixpanel), funnels, cohort analysis, retention metrics, North Star Metrics, and OKR setting. Use this skill to define data-driven product strategies, optimize user journeys, and build robust performance dashboards.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.